{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:5AAOVIALPHIF3EADC462UGR4JV","short_pith_number":"pith:5AAOVIAL","canonical_record":{"source":{"id":"1512.06216","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-19T09:55:37Z","cross_cats_sorted":["cs.CV","cs.DC"],"title_canon_sha256":"982945ef8910566cf7718bb4cc59d6082e9097a9e9c0e85a41d7cd06c6d19f16","abstract_canon_sha256":"8e58f997b2826fe443b25baaf7c022ba6f96f2ca0cd81f1a0fb415505fa32240"},"schema_version":"1.0"},"canonical_sha256":"e800eaa00b79d05d9003173daa1a3c4d7358d4e01d4d1f5356aca646d3c62fd2","source":{"kind":"arxiv","id":"1512.06216","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.06216","created_at":"2026-05-18T01:24:01Z"},{"alias_kind":"arxiv_version","alias_value":"1512.06216v1","created_at":"2026-05-18T01:24:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.06216","created_at":"2026-05-18T01:24:01Z"},{"alias_kind":"pith_short_12","alias_value":"5AAOVIALPHIF","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"5AAOVIALPHIF3EAD","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"5AAOVIAL","created_at":"2026-05-18T12:29:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:5AAOVIALPHIF3EADC462UGR4JV","target":"record","payload":{"canonical_record":{"source":{"id":"1512.06216","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-19T09:55:37Z","cross_cats_sorted":["cs.CV","cs.DC"],"title_canon_sha256":"982945ef8910566cf7718bb4cc59d6082e9097a9e9c0e85a41d7cd06c6d19f16","abstract_canon_sha256":"8e58f997b2826fe443b25baaf7c022ba6f96f2ca0cd81f1a0fb415505fa32240"},"schema_version":"1.0"},"canonical_sha256":"e800eaa00b79d05d9003173daa1a3c4d7358d4e01d4d1f5356aca646d3c62fd2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:24:01.157348Z","signature_b64":"4UUAZ+lKL5IX2aAGELVw5SY9Rlam/IW14jNFn+tWdNpeJ5ulRgbF9rKqkYXX5esi9dMRGJ0l72Buas91jkr/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e800eaa00b79d05d9003173daa1a3c4d7358d4e01d4d1f5356aca646d3c62fd2","last_reissued_at":"2026-05-18T01:24:01.156619Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:24:01.156619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1512.06216","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:24:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WPgPTSSnu0fwZ47K5gaT4hOafCyknsln7TwFUmQ6b8ilokOsCNHEz9zq7JJMvtuWlxpUsvHdq9RhuypxBHJ2Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:38:04.368598Z"},"content_sha256":"1c9b6d4c65561c7de3f47cb2443a575c7ac3f690e80a8e4dac6abf98b9810f41","schema_version":"1.0","event_id":"sha256:1c9b6d4c65561c7de3f47cb2443a575c7ac3f690e80a8e4dac6abf98b9810f41"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:5AAOVIALPHIF3EADC462UGR4JV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.DC"],"primary_cat":"cs.LG","authors_text":"Eric Xing, Gunhee Kim, Hao Zhang, Jinliang Wei, Pengtao Xie, Qirong Ho, Zhiting Hu","submitted_at":"2015-12-19T09:55:37Z","abstract_excerpt":"Deep learning (DL) has achieved notable successes in many machine learning tasks. A number of frameworks have been developed to expedite the process of designing and training deep neural networks (DNNs), such as Caffe, Torch and Theano. Currently they can harness multiple GPUs on a single machine, but are unable to use GPUs that are distributed across multiple machines; as even average-sized DNNs can take days to train on a single GPU with 100s of GBs to TBs of data, distributed GPUs present a prime opportunity for scaling up DL. However, the limited bandwidth available on commodity Ethernet n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.06216","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:24:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/gNHv5rv2frhxE75eVBrB71I+MoJcaLcPqTm/eRGDjCXcaNRnPLjpGvGbeDAaMxhYeuWHpeFzdc4+9IQwegGCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:38:04.368951Z"},"content_sha256":"00120079fcfd9e7d7df3cc7169a7b17fbb63e820345f9383be0632c7d46bd81f","schema_version":"1.0","event_id":"sha256:00120079fcfd9e7d7df3cc7169a7b17fbb63e820345f9383be0632c7d46bd81f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5AAOVIALPHIF3EADC462UGR4JV/bundle.json","state_url":"https://pith.science/pith/5AAOVIALPHIF3EADC462UGR4JV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5AAOVIALPHIF3EADC462UGR4JV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T10:38:04Z","links":{"resolver":"https://pith.science/pith/5AAOVIALPHIF3EADC462UGR4JV","bundle":"https://pith.science/pith/5AAOVIALPHIF3EADC462UGR4JV/bundle.json","state":"https://pith.science/pith/5AAOVIALPHIF3EADC462UGR4JV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5AAOVIALPHIF3EADC462UGR4JV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:5AAOVIALPHIF3EADC462UGR4JV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8e58f997b2826fe443b25baaf7c022ba6f96f2ca0cd81f1a0fb415505fa32240","cross_cats_sorted":["cs.CV","cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-19T09:55:37Z","title_canon_sha256":"982945ef8910566cf7718bb4cc59d6082e9097a9e9c0e85a41d7cd06c6d19f16"},"schema_version":"1.0","source":{"id":"1512.06216","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.06216","created_at":"2026-05-18T01:24:01Z"},{"alias_kind":"arxiv_version","alias_value":"1512.06216v1","created_at":"2026-05-18T01:24:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.06216","created_at":"2026-05-18T01:24:01Z"},{"alias_kind":"pith_short_12","alias_value":"5AAOVIALPHIF","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"5AAOVIALPHIF3EAD","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"5AAOVIAL","created_at":"2026-05-18T12:29:05Z"}],"graph_snapshots":[{"event_id":"sha256:00120079fcfd9e7d7df3cc7169a7b17fbb63e820345f9383be0632c7d46bd81f","target":"graph","created_at":"2026-05-18T01:24:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Deep learning (DL) has achieved notable successes in many machine learning tasks. A number of frameworks have been developed to expedite the process of designing and training deep neural networks (DNNs), such as Caffe, Torch and Theano. Currently they can harness multiple GPUs on a single machine, but are unable to use GPUs that are distributed across multiple machines; as even average-sized DNNs can take days to train on a single GPU with 100s of GBs to TBs of data, distributed GPUs present a prime opportunity for scaling up DL. However, the limited bandwidth available on commodity Ethernet n","authors_text":"Eric Xing, Gunhee Kim, Hao Zhang, Jinliang Wei, Pengtao Xie, Qirong Ho, Zhiting Hu","cross_cats":["cs.CV","cs.DC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-19T09:55:37Z","title":"Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.06216","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1c9b6d4c65561c7de3f47cb2443a575c7ac3f690e80a8e4dac6abf98b9810f41","target":"record","created_at":"2026-05-18T01:24:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8e58f997b2826fe443b25baaf7c022ba6f96f2ca0cd81f1a0fb415505fa32240","cross_cats_sorted":["cs.CV","cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-19T09:55:37Z","title_canon_sha256":"982945ef8910566cf7718bb4cc59d6082e9097a9e9c0e85a41d7cd06c6d19f16"},"schema_version":"1.0","source":{"id":"1512.06216","kind":"arxiv","version":1}},"canonical_sha256":"e800eaa00b79d05d9003173daa1a3c4d7358d4e01d4d1f5356aca646d3c62fd2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e800eaa00b79d05d9003173daa1a3c4d7358d4e01d4d1f5356aca646d3c62fd2","first_computed_at":"2026-05-18T01:24:01.156619Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:24:01.156619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4UUAZ+lKL5IX2aAGELVw5SY9Rlam/IW14jNFn+tWdNpeJ5ulRgbF9rKqkYXX5esi9dMRGJ0l72Buas91jkr/DA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:24:01.157348Z","signed_message":"canonical_sha256_bytes"},"source_id":"1512.06216","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c9b6d4c65561c7de3f47cb2443a575c7ac3f690e80a8e4dac6abf98b9810f41","sha256:00120079fcfd9e7d7df3cc7169a7b17fbb63e820345f9383be0632c7d46bd81f"],"state_sha256":"f3b66d661ddf7eca0022e5abdd58be3a40d10ddeb3b450c69282d2a69ea87fff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W9bjfFNjLTTP+JgSxByTw8QdbB1NFtUcR6T83P5wrHOP3R93n9nQsSVLhasDplV8qKNYYgvXaXztwcEbLgGxCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T10:38:04.371245Z","bundle_sha256":"deebea4ad75630fac680fee0c4b40b3e8b9df8352d8e0ccf8b0eccb115839419"}}